Operators of industrial machines, vehicles, mobile devices—or technical equipment in general—need to ensure proper and safe operation of the equipment. As some constructional components can suddenly fail, sensors can alert the operator in critical situations. For example, the technical equipment can be a turbine in a power plant. Dedicated sensors are provided at bearings and other critical components of the turbine. Computers process the sensor data to alert the operator—often in situations that have become critical already.
Processing data from further sensors could help to detect or to predict behavioral trends of the equipment. This allows alerting the operator even if the situation is not yet critical. In the example, a non-typical rotation of the turbine rotor might be detected by evaluating data from sensors in the proximity of the turbine. Such data includes acoustical data from a microphone sensor.
However, the multi-sensor approach is not free of technical problems. First, the sensors produce huge amounts of data. The order of magnitude of data can exceed some gigabytes per second. Processing data by a central processing unit (CPU) requires a high-bandwidth infrastructure between the sensors and the CPU to communicate the data to the CPU. Further, the time needed for processing is in contract with the operator's desire to see results in real-time. Second, different processing applications would use the same data to identify different trends. In the turbine example, a first processing application identifies non-typical rotation of the rotor, and a second processing application identifies non-typical oscillations in the blades. But using the same data requires intermediate storage of the data, again with computer infrastructure requirements.